Using Google's Aggregate and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco

With urban freeway congestion on the rise and limited funds for highway expansion, it may be essential to manage future traffic growth using high occupancy toll (HOT) lanes and other travel demand management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, information describing trip origins and destinations by time-of-day was desired. Observed roadway facility-specific origin-destination (OD) flows can help understand spatial distribution of demand and impute willingness-to-pay, which are useful to evaluate various TDM strategies. Historically this was only available through expensive and time-consuming license plate or intercept surveys. This paper describes a new passively-collected data source – Google’s Aggregate and Anonymized Trip (AAT) Data – obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering the 9-county Bay Area specific to four freeway segments in San Francisco were obtained. Since AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT dataset and observed traffic volumes from Caltrans’Performance Measurement System (PeMS). The models were applied to convert relative flows to trips and derive facility-specific time-dependent OD matrices. Comparison of these facility-specific OD matrices to select link OD matrices from SF-CHAMP (an activity-based travel demand model for the region) showed that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the OD flow level. Some opportunities and limitations of the new data source are discussed along with recommendations for future research.

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  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Sana, Bhargava
    • Castiglione, Joseph
    • Cooper, Drew
    • Tischler, Daniel
  • Conference:
  • Publication Date: 2017

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 16p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01626179
  • Record Type: Publication
  • Report/Paper Numbers: 17-00437
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 8 2016 10:04AM